Commercial credit evaluation typically is a process that requires a great deal of expertise. The existing approach to the credit process may consist of a number of primarily manually-driven steps. These steps can include: (1) Acquisition, the process of receiving the loan application, and pipelining and tracking the application; (2) Pre-Underwriting, a process typically conducted by service officers, including searching databases and other computer assisted activities; (3) Underwriting, which is typically conducted by credit officers and other personnel with computer assistance, including spreading of business financials, assigning of debt rating, and evaluation of risk; (4) Approval, typically conducted by a credit officer, which involves reviewing the package, obtaining signatures, and making decision notification; (5) Documentation, including creation of appropriate legal documents; (6) Booking, including typically computer assisted ledger recording and disbursing of funds; (7) Maintenance, which is usually conducted by personnel with computer assistance, including monitoring the portfolio and performing reviews; (8) Collection, which includes classified credit reporting and remedial management; and (9) Management Information System (MIS), typically computer systems that create reports.
Present primarily personnel-heavy elements of the process for determining credit can be very inefficient. For example, the turnaround time for loan application packages can often be several weeks or more. In addition, inconsistencies may result from the subjective nature of the process, even when personnel are highly trained and use clear guidelines. Further, the decisionmaking process involves an extremely wide range of possible decisions, which can often require a great deal of experience.
As a result of these factors, conservative decisions may sometimes be made by the credit officer, resulting in loss of business. Conversely, poor decisions are sometimes made which result in loan loss.
A useful method for assisting with the decisionmaking process such as that necessary for loan analysis is artificial intelligence systems. Artificial intelligence systems first began to be developed in the early 1950s, beginning in the medical diagnostics field. In the 1980s, tremendous growth in application of artificial intelligence occurred, including application to the finance arena; this growth has resurged in the 1990s.
A particular type of artificial intelligence application is an expert system. Expert systems are used for problem solving areas such as diagnosis, analysis, and classification and are good at dealing with ill-defined problems. A number of different approaches using expert systems have been taken in dealing with either the credit recommendation or the decision type of problem as applied to loan analysis. These approaches include decision trees and evidence trees. Typically existing methods for using decision trees is to apply a series of gates with probabilities and degrees of certainty to the gates. Based on the results of these gate analyses, a recommendation can be developed.
Another approach deals with loan analysis on a case-specific reasoning basis; this approach is often referred to as frame-based, a technical term for making actual comparisons to established norms.
Yet another approach is to use simple types of procedural rule-based analysis, such as a series of rules. In this approach, each yes or no answer to a particular rule results in another rule, depending on the answer. By stringing together such rules and answers, a simplified credit evaluation can be developed.
An example of existing art for an expert system for credit evaluation is O. Castillo and P. Melin, An Expert System for Credit Evaluation, (IN Proceedings; The Second Annual International Conference on Artificial Applications on Wall Street: Tactical and Strategic Computing Technologies, Proceedings of AI Applications on Wall Street, April 1993, Software Engineering Press). This system is a very simple procedural rule-based approach that utilizes a series of questions for which the user answers yes or no. Drawbacks of the system include that it is inflexible and not easily transferrable to other applications. It contains no graphical interfaces that ease user understanding of the loan application process. No explanations are included.
Another example of existing art is R. Beshinske, et al., Margin Credit Evaluation System (IN Proceedings; The First International Conference on Artificial Intelligence on Wall Street (Cat. No. 91TH0399-6) October 1991). This system evaluates a portfolio of securities for an individual company based on a one-time up front review. The system utilizes a weighting of factors based on a statistical regression technique. The system was not designed for loan analysis. It does not provide the user with guidance or extensive graphical analysis assistance with decisionmaking.
An example of existing patented art is Saladin, et al., Expert Credit Recommendation Method and System, U.S. Pat. No. 5,262,941. This system utilizes a series tables and a decision matrix to determine an overall credit recommendation. The system requires input of information by a user in a particular predetermined series of queries. Information cannot be provided out of sequence. The system uses a scoring system to proceed through the series of tables and decision matrix in reaching a decision. No weighting method is used for scoring the loan application.
In general, many of these approaches are very simple, using only very basic types of processes. In addition, the approaches to loan analysis that have been developed in the existing art are only applicable to very limited sets of circumstances, such as determining margin loans, rather than commercial credit situations. They also do not provide extensive assistance to the user in understanding the factors that make up the decisionmaking process or explain their purpose. In general, they are not designed with a graphical or otherwise user-friendly format.